The Computational Theory of Mind

Over the past thirty years, it is been common to hear the mind likened
to a digital computer. This essay is concerned with a particular
philosophical view that holds that the mind literally is a digital
computer (in a specific sense of “computer” to be
developed), and that thought literally is a kind of computation. This
view—which will be called the “Computational Theory of
Mind” (CTM)—is thus to be distinguished from other and
broader attempts to connect the mind with computation, including (a)
various enterprises at modeling features of the mind using
computational modeling techniques, and (b) employing some feature or
features of production-model computers (such as the stored program
concept, or the distinction between hardware and software) merely as a
guiding metaphor for understanding some feature of the mind. This
entry is therefore concerned solely with the Computational Theory of
Mind (CTM) proposed by Hilary Putnam [1961] and developed most notably
for philosophers by Jerry Fodor [1975, 1980, 1987, 1993]. The senses
of ‘computer’ and ‘computation’ employed here
are technical; the main tasks of this entry will therefore be to
elucidate: (a) the technical sense of ‘computation’ that
is at issue, (b) the ways in which it is claimed to be applicable to
the mind, (c) the philosophical problems this understanding of the
mind is claimed to solve, and (d) the major criticisms that have
accrued to this view.

The Computational Theory of Mind combines an account of reasoning
with an account of the mental states. The latter is sometimes called
the Representational Theory of Mind (RTM). This is the thesis that
intentional states such as beliefs and desires are relations between a
thinker and symbolic representations of the content of the states: for
example, to believe that there is a cat on the mat is to be in a
particular functional relation (characteristic of the attitude of
belief) to a symbolic mental representation whose semantic value is
“there is a cat on the mat”; to hope that there is a cat on the mat is
to be in a different functional relation (characteristic of the
attitude of hoping rather than of believing) to a symbolic mental
representation with the same semantic value.

The thesis about reasoning, which we will call the Computational
Account of Reasoning (CAR), depends essentially upon this prior claim
that intentional states involve symbolic representations. According to
CAR, these representations have both semantic and syntactic properties,
and processes of reasoning are performed in ways responsive only to the
syntax of the symbols—a type of process that meets a technical
definition of ‘computation’, and is known as formal symbol
manipulation. (I.e., manipulation of symbols according to purely
formal—i.e., non-semantic—techniques. The word ‘formal’
modifies ‘manipulation’, not ‘symbol’.)

These notions of “formal symbol manipulation” and “computation” are
technical, and ultimately derive from discussions in mathematics in the
late 19th and early 20th centuries. The project
of formalization began in response to a crisis that developed in
mathematics upon the discovery that there were consistent geometries
that denied Euclid's parallel postulate. (I.e., the claim that for any
line L on a plane and any point P on that plane but not located along
L, there is one and only one line through P parallel
to L.) The parallel postulate's overwhelming plausibility
was not based upon anything else that was explicit in Euclid's system,
but upon deeply seated geometric/spatial intuitions. It had long been
assumed that such intuitively-correct claims in Euclidean geometry
were necessarily true in the sense that they could not consistently be
denied. The discovery in the early 19th century of
consistent geometries that were not consonant with our spatial
intuitions prompted mathematicians like Gauss, Peano, Frege and
Hilbert to seek ways to regiment mathematical reasoning so that all
derivations were grounded in explicit axioms and rules of inference,
and the semantic intuitions of the mathematician were either excluded
or explicitly codified. The most influential strategy for
formalization was that of Hilbert, who treated formalized reasoning as
a “symbol game”, in which the rules of derivation were
expressed in terms of the syntactic (or perhaps better, non-semantic)
properties of the symbols employed.

One of the powerful results of the formalist program was the
discovery that large swaths of mathematics can in fact be formalized in
this way—i.e., that the semantic relationships intuitively deemed
important in a domain like geometry can in fact be preserved by
inferences sensitive only to the syntactic form of the expressions.
Hilbert himself carried out such a project with respect to geometry,
and Whitehead and Russell extended such a method to arithmetic. And
this project served as a model for other, ultimately less successful,
reductive projects outside of mathematics, such as logical behaviorism
in psychology. Even in mathematics, however, there are limits to what
can be formalized, the most important and principled of which are
derivative from Godel's incompleteness proof.

A second important issue in nineteenth and early twentieth century
mathematics was one of delimiting the class of functions that are
“computable” in the technical sense of being decidable or
evaluable by the application of a rote procedure or
algorithm. (Familiar examples of algorithmic procedures would be
column addition and differential equations.) Not all mathematical
functions are computable in this sense; and while this was known by
mathematicians in the 19th century, it was not until 1936
that Alan Turing proposed a general characterization of the class of
computable functions. It was in this context that he proposed the
notion of a “computing machine”—i.e., a machine
that does things analogous to what a human mathematician does in
“computing” a function in the sense of evaluating it by
application of a rote procedure. Turing's proposal was that the class
of computable functions was equivalent to the class of functions that
could be evaluated in a finite number of steps by a machine of the
design he proposed. The basic insight here was that
any operations that are sensitive only to syntax can be duplicated
(or perhaps simulated) mechanically. What the mathematician
following a formal algorithm does by way of recognition of syntactic
patterns as syntactic, a machine can be made to do by purely
mechanical means. Formalization and computation are thus closely
related, and together yield the result that reasoning that can be
formalized can also be duplicated (or simulated) by the right type of
machine. Turing himself seems to have been of the opinion that a
machine operating in this way would literally be doing the same things
that the human performing computations is doing—that it would
be “duplicating” what the human computer does. But other
writers have suggested that what the computer does is merely a
“simulation” of what the human computer does: a
reproduction of human-level performance, perhaps through a set of
steps that is some level isomorphic to those the human undertakes, but
not in such a fashion as to constitute doing the same thing in all
relevant respects. (For example, one might take the human computer's
awareness that the symbols are symbols of something as
partially constitutive of the operation counting as a
computation.)

As mentioned more informally at the outset, CTM combines a
Representational Theory of Mind (RTM) with a Computational Account of
Reasoning (CAR). The RTM is in this case informed by the notion of
symbolic representation employed in the technical notion of
computation: mental states are held to be
“representational” in the sense of including, as
constituents, symbolic representations having both semantic and
syntactic properties, just as symbols employed in mathematical
computations do. While this claim differs from early modern versions
of representationalism that likened ideas to pictures rather than
symbols, it becomes philosophically important only in conjunction with
the CAR. According to this account, reasoning is a process in which
the causal determinants are the syntactic properties of the symbols in
the “language of thought” (LOT) or
“mentalese”.

The technical notions of formalization and computation arguably do
some important philosophical work here: formalization shows us how
semantic properties of symbols can (sometimes) be encoded in
syntactically-based derivation rules, allowing for the possibility of
inferences that respect semantic value to be carried out in a fashion
that is sensitive only to the syntax, and bypassing the need for the
reasoner to have employ semantic intuitions. In short, formalization
shows us how to tie semantics to syntax. Turing's notion of a computing
machine, in turn, shows us how to link up syntax to causation, in that
it is possible to design a mechanism that is capable of evaluating any
formalizable function.

The most obvious domain for CTM is that of occurrent propositional
attitude states—that is, states that occur at some specific moment
in a person's mental life, and have the sort of content that might be
expressed by a propositional phrase, such as a judgment that the cat is
at the door or a desire that the cat would stop tearing at the screen.
Here we perhaps have the most plausible cases of mental states that
might be grounded in something like token mental representations.

Within this class of occurrent states, however, we may additionally
distinguish between the kinds of states that occur in explicit,
conscious judgments and mental states that are not conscious because
they take place at a “level of processing” that is too low to be
brought to conscious awareness—e.g., processes of contour detection
in early vision. (Such processes might be called“infraconscious” in
distinction to the subconscious or unconscious processes championed by
Freud and Jung.) (Cf. Horst 1995.) Many advocates of CTM apply the
theory, not only at the level of explicit judgements and occurrent
desires, but also to a broad array of infraconscious states as
well.

However, advocates of CTM often speak of it more generally as an
account of beliefs and desires which are then cashed out in
dispositional rather than occurrent terms. Such states are arguably
more problematic for CTM than occurrent states, as there are many
things one might be thought to “believe” or
“desire” in the dispositional senses of those terms, but
which could not plausibly be supposed to be explicitly represented in
the form of a symbol token.

An additional issue regarding the intended scope of the theory is that
of how comprehensive an account of mental states and
processes it is intended to be. Advocates of CTM and critics alike
have often assumed that CTM makes claims to be a quite general account
of reasoning. This is complicated, however, by Fodor's [1984]
distinction between “modular” and “global”
mental processes, and his judgement (in Fodor [2000]) that it is only
the former that are likely to be computational in the classic
sense. While this view has struck some readers as surprising, Fodor
claims that, while advocating the truth of CTM since the 1970s, it
“hadn't occurred to [him] that anyone could think that it's a
very large part of the truth; still less that it's within
miles of being the whole story about how the mind works.” [2000,
page 1] We may therefore refine questions about the truth of CTM to
questions about its truth as a theory of particular kinds of
mental processes.

The notion of “computation” that has been described above,
based in the work of Turing and Church, has been widely used by both
advocates of CTM [Fodor 1981, 1987; Pylyshyn 1980, 1984; Haugeland
1978, 1981] and their critics [Searle 1980, 1984, 1992; Dreyfus 1972,
1992; Horst 1996, 1999]. There are, however, other notions of
“computation” that have figured in the histories both of
computer science and of cognitive science.

1.4.1 Human Problem-Solving

Turing's seminal article, “On Computable Numbers”, builds
a new formal notion of “computation” upon the model of
what a human mathematician does in solving a problem through
application of an algorithm. It is interesting to note, however, that
in the article, Turing uses the word ‘computer’ only for
human beings performing such operations. Turing seems to have viewed
the evaluation of functions through algorithmic means as something
that both humans and machines are capable of doing. However, his
usage in this paper still reflects an older usage of the word
‘computation’ in mathematics. Critics of CTM who view
such operations, when performed by humans, as involving rich
intentional states may contest the assumption that computing machines
actually “compute” in this older sense, even if they
perform operations on non-intentional states that mirror the formal
properties of computations performed by humans.

1.4.2 Church and von Neumann

While Turing has been given pride of place in the history of digital
computation, similar ideas were being introduced at about the same
time by Alonzo Church [1936] and Otto von Neumann [1945]. The proofs
of Turing and Church are widely regarded as equivalent, and referred
to as “the Church-Turing thesis”. Von Neumann provided an
abstract architecture for a computing machine that is significantly
different from Turing's at an engineering level, and production-model
computers more closely resemble Von Neumann's architecture than a
Turing Machine. However, from a mathematical standpoint, it was shown
that any function that is computable by either type of machine is also
computable by the other [Minsky 1967].

1.4.3 McCulloch and Pitts

Warren McCulloch and Walter Pitts [1943] developed an importantly
different type of computing machine, whose architecture was more
directly inspired by what they saw to be similarities between neural
and digital circuits. McCulloch and Pitts employed an architecture
consisting of a network of nodes connected by links, which they saw as
paralleling the connective structure of the brain. They viewed
representations as activation patterns in such a network, and treated
the nodes themselves as neither symbolic nor representational. This
approach is widely viewed as the ancestor of an alternative research
programme in AI, sometimes called “connectionism”
[Rosenblatt 1957; Rumelhart and McClelland 1987; see also the entries on
mental representation,
connectionism].

1.4.4 Analog Computation

“Digital computation” is often contrasted with
“analog computation”. The expression ‘analog
computation’ has both a narrow technical meaning and a more
general application. The colloquial meaning turns upon the notion
that some machines have components that represent information in a
fashion that is analogous to what is represented. (For example, use
of a compass dial to represent geographic directions.) The technical
meaning involves a contrast with digital systems, where
“digital” means that the individual circuits are each
capable of only a finite number of discrete states. (For example, a
numerical value of 0 or 1.) “Analog”, in the narrow
sense, simply means “not digital”, and applies to systems
whose components are capable of a continuum of states. (For example,
numerical values consisting of all of the real numbers from 0 to 1.)
In this technical sense, “analog” systems need not be
analogous to what they represent.

For purposes of technical accuracy, it should also be noted that
“digital” is often erroneously conflated with
“binary”. A digital system can have any (finite) number
of discrete values. While production-model computers employ a binary
system (one with two values, represented as 0 and 1), a three-valued
(or n-valued) system (say, with values represented as 0, 1, and 2)
would also count as digital.

1.4.5 Computational Neuroscience

There is an area of cognitive science called “cognitive
neuroscience”. Researchers in this area are interested in
neuroscience, and hence do not treat cognition in abstraction from the
“implementation level”. However, they, and many other
neuroscientists, often affirm that “the brain is a
computer”. Just what this means, however, is often unclear. It
may mean nothing more than that the brain is involved in
information-processing that can be described in algorithmic terms,
without a further commitment to the thesis that such
information-processing is accomplished through the application of
algorithms to symbolic representations. Many physical and biological
processes can likewise be characterized in algorithmic terms, but such
descriptions are essentially attempts to state laws governing
mechanisms, and are “computational” in the sense that
would license speaking of all physical or biological systems as
computers.

CTM rose to prominence as one of the most important theories of mind
in the 1980s. This may in part have been due to the intuitive
attraction of the computer metaphor, which played upon the notion of a
technology that was rapidly gaining public recognition and
technological applications. By this time, moreover, the computer had
influenced the understanding of the mind through the influence some
projects in the sciences of cognition (such as David Marr's model of
vision (Marr 1983)) and in artificial intelligence, where researchers
sought to endow machines with human-level competences in reasoning,
language, problem-solving and perception, though not always by
replicating the mechanisms by which these are performed in humans. In
addition, CTM's advocates also claimed that it provided solutions to
several important philosophical problems, and its plausibility in these
areas was an important contributor to its rapid rise to popularity.

The most important philosophical benefit claimed for CTM was that it
purported to show how reasoning could be a non-mysterious sort of
causal process, and could nonetheless be sensitive to semantic
relations between judgments. The background problem here was the
received view that reasons are not causes. On the one hand, it is hard
to see how a purely causal process could proceed on the basis
of the semantic values of propositions. To posit a mechanism that
understood the meanings of mental symbols would in effect be to posit a
little interpreter or homunculus inside the head, and then the
same problems of coordinating reason and causation would recur for the
homunculus, resulting in a regress of interpreters. On the other hand,
it is hard to see how a process specified in purely causal terms could
thereby count as a reasoning process, as calling something “reasoning”
locates it with respect to norms and not merely to causes. (That is, to
call a process “rational” is not merely to describe its causal
etiology, but to say that it meets, or at least is evaluable by,
certain standards of reasoning, such as validity.)

CTM (or, more specifically, the CAR) can be seen as a compatibility
proof, showing the compatibility of intentional realism (i.e.,
a commitment to the reality of the semantic properties of mental
states, and to the causal roles of mental states in the determination
of behavior) with the claim that mental processes are all causal
processes for which a causal mechanism could, in principle, be
specified. The trick to linking semantics to causation is to link them
both intermediately to syntax. Formalization shows us how to link
semantics to syntax, and computation shows us how to link syntax to
causal mechanisms. Therefore, there is a consistent model on which bona
fide reasoning processes (processes that respect the semantic values of
the terms) can be carried out through non-mysterious physical
mechanisms: namely, if the mind is a computer in the sense that its
mental representations are such that all semantic properties are
tracked by corresponding syntactic properties that can be exploited by
the “syntactic engine” (Haugeland 1981) that is causally responsible
for reasoning.

A compatibility proof is in itself weak evidence for the truth of a
theory. However, through the 1980s and 1990s, many philosophers were
convinced by Fodor's claim that CTM is “the only game in
town”—i.e., that the only accounts we have of cognitive
processes are computational, and that this implies the postulation of
a language of thought and operations performed over the
representations in that language. Given this argument that CTM is
implicit in the theories produced by the sciences of cognition (see
below), its additional ability to provide a compatibility proof for
physicalism and intentional realism solidified its philosophical
credentials by showing that this interpretation of the sciences of
cognition was philosophically productive as well.

In addition to the compatibility proof, some philosophers viewed
CTM—or more precisely, RTM—as providing an explanation of
the semantic properties of mental states as well. Fodor, for example,
claims that, just as public language utterances inherit their semantic
properties from the thoughts of the speaker, thoughts inherit their
semantic properties from the mental representations in a LOT that are
among their constituents. If I have a thought that refers to Bill
Clinton, it is because that thought is a relation to a mental
representation that refers to Bill Clinton. If I think “Clinton
was President in 1995” it is because I am in a particular
functional relation (characteristic of belief) that has the content
“Clinton was President in 1995”.

Within this general view of the semantics of mental states, however,
there are at least three variant positions, here arranged from weakest
to strongest.

Given an adequate account of the semantics of mental
representations, one does not then need a further account of the
semantics of intentional states, save for the fact that they
“inherit” their semantic values from those of their
constituent representations. (This view is absolutely neutral as to
the nature of an adequate account of the semantics of mental
representations. Indeed it is neutral about the prospects of such an
account: it claims that
given such a semantics for mental representations, no further
work is needed for a semantics of intentional states.) [see
criticisms]

The claim that mental representations are symbolic
representations is supposed to provide an account of their semantic
nature: i.e., a mental representation is said to be “about
Clinton” in exactly the same sense that other symbols (e.g.,
public language symbols) are said to be “about
Clinton”. Thus, if one thinks there is already an adequate
semantics for symbols generally (e.g., Tarskian semantics), no further
semantic account is needed to cash out what it is for a mentalese
symbol to have semantic properties. [see criticisms]

The claim that the semantic properties of the symbols are explained
by or supervene upon the syntactic properties. (This claim was never
endorsed by major proponents of CTM such as Putnam, Fodor or Pylyshyn,
and is probably best understood as a misunderstanding of CTM.) [see
criticisms]

In addition to these potential contributions to philosophy of mind,
CTM was at the same time in a symbiotic relationship with applications
of the view of the mind as computer in artificial intelligence and the
sciences of cognition. On the one hand, philosophical formulations such
as CTM articulated a general view of mind and computation that was
congenial to many researchers in AI and cognitive science. On the other
hand, the successes of computational models of reasoning, language and
perception lent credibility to the idea that such processes might be
accomplished through computation in the mind as well.

Two connections with empirical research stand out as of particular
historical importance. The first connection is with Chomskian
linguistics. Chomsky introduced a “cognitivist revolution” in
linguistics that displaced the then-prevalent behaviorist understanding
of language-learning. The latter, argued Chomsky [1959], was incapable
of accounting for the fact that a child latches on to grammatical
rules, and is then able to apply them in indefinitely many novel
contexts, in ways underdetermined by the finite set of stimuli s/he has
been exposed to. This, argued Chomsky, required the postulation of a
mechanism that did not work simply on general principles of classical
and operant conditioning, but was specifically optimized for
language-learning. Chomskian linguists often spoke of the child's
efforts at mastery of a grammar in terms of the formation and
confirmation of hypotheses; and this, argued Fodor [1975], required an
inner language of thought. Chomskian linguistics was thus viewed as
requiring at least RTM, and computationalists took it as plausible that
the mechanisms underlying hypothesis-testing could be cashed out in
computational terms.

Chomskian grammar also stressed features of linguistic competence such
as systematicity (the person who is able to understand the
sentence “the dog chased the cat” is able to understand
the sentence “the cat chased the dog” as well)
and productivity (the ability of a person to have an infinite
number of thoughts generated from a finite set of lexical primitives
and recursive syntactic rules). Thought, of course, also possesses
these features. From a computationalist perspective, these two facts
are not accidentally linked: natural language is systematic and
productive because it is an expression of the thoughts of a mind that
already possesses systematicity and productivity; the mind possesses
these features, in turn, because thought takes place in a
syntactically-structured representational system. Indeed, argues
Fodor, a syntactically-structured language is the only known
way of securing these features, and so there is strong prima
facie reason to believe that RTM is true.

The second important link with cognitive science is with David Marr's
theory of vision. Marr [Marr 1982; Marr and Poggio 1977] pioneered a
computational approach to vision. While few of the details of their
account have survived into current work in the science of vision, what
was most influential about their work was not the empirical details
but a set of powerful metatheoretical ideas. Marr distinguished
between three levels that needed to be distinguished in a
theory of vision (or, by extension, other cognitive processes.) At the
highest level was a specification of what task a system was designed
to perform: e.g., in the case of vision, to construct a
three-dimensional representation of distal stimuli on the basis of
inputs to the retina. This level Marr (somewhat unfortunately) called
the “computational level”. At the other end of the
spectrum was a level describing the “implementation” of
this function by the “hardware” of the system (e.g., the
neurochemical properties underlying phototransduction in retinal
cells). These two levels alone present a conventional functionalist
picture. But in between them Marr inserted an
“algorithmic” level of explanation. Here the task of the
theorist was to isolate a plausible candidate for the algorithm the
system was employing in performing the task—an algorithm that
must both be appropriate to the task specified at the
“computational” level and compatible with the neurological
facts at the “implementational” level.

Such an intermediate algorithmic level is of course closely related
to a strategy for modeling visual processes: the modeler
starts with psychophysical data (say, the Weber laws) and then attempts
to construct models that have isomorphic input/output conditions. As
computational models, the work in these is done by the
algorithms used to transform input into output. While it is possible
to view such modeling as simply on a par with, say, the modeling of
weather systems (in which there is no assumption that what is
modeled is in any interesting sense “algorithmic” or
“computational”) the availability of computational modeling techniques
also suggests the hypothesis that visual processes themselves are
accomplished algorithmically, as algorithmic methods are at least
among the available ways of accomplishing the informational
tasks involved in the psychophysical data. There is, of course, a
familiar philosophical ambiguity lurking in the wings here—the
confusion of behaving in a fashion describable by a rule with
following or applying a rule—and arguably advocates of an
algorithmic level of description have not always kept this distinction
in mind. Nonetheless, with this caveat (i.e., the unclarity of whether
the algorithmic level is simply a level of description or whether the
system is said to be applying an algorithm), some version of
Marr's three-level approach quickly became something of an orthodoxy in
cognitive science in the 1980s.

Marr's approach has obvious connections with CTM. Both involve inner
representations and algorithmic processes that mediate transformations
from one representation to another. The growth of models employing
Marr's three-tiered approach in the sciences of cognition seemed to
provide empirical support for the view that the mind is an algorithmic
symbol-processor. But research like Marr's also suggested a moral for
RTM and CTM that made them, in a way, potentially more radical. One
might have held RTM and CTM only as theories of the kinds of mental
processes that can be articulated in sentences in a
natural-language—“high level” processes like
conscious thoughts. Marr's algorithms, however, apply at a much
simpler level, such as the information processes going on between two
levels of cells in the visual system. Such processes are not subject
to conscious inspection or intervention, and could not be reported in
natural language by the speaker. Such a theory therefore involves the
postulation of a host of symbols and algorithms that are not so
much unconscious as
infraconscious—that is, processes that take place at a far
simpler level than what philosophers have been accustomed to thinking
of as “thoughts”.

The strongest proposed relationship between the syntax and semantics
of symbols—that semantic properties supervene upon syntactic
properties—was, as mentioned, never embraced by the major
proponents of CTM. Indeed, Putnam (1980) pointed out a major obstacle
to such a view. It consists in a consequence of the Lowenheim-Skolem
theorem in logic, from which it follows that every formal symbol
system has at least one interpretation in number theory. This being
the case, take any syntactic description D of Mentalese. Because our
thoughts are not just about numbers, a canonical interpretation of the
semantics of Mentalese (call it S) would need to map at least
some of the referring terms onto non-mathematical objects. However,
Lowenheim-Skolem assures that there is at least one
interpretation S* that maps all of the referring terms onto
only mathematical objects. S* cannot be the canonical
interpretation, but there is nothing in the syntax of
Mentalese to explain why S is the correct interpretation
and S* is not. Therefore syntax underdetermines
semantics. (Compare acknowledgment of this in Pylyshyn 1984: page
44.)

Fodor (1981) has proposed that the view that mental states are
relations to symbolic representations is supposed to explain how
mental states come to have semantic values and intentionality. This
is, he claims, because it is mental representations that have these
properties “in the first instance,” while propositional
attitude states “inherit” them from the mental
representations that are among their constituents. This view was
criticized by Searle (1980, 1984) and Sayre (1986, 1987), and the line
of criticism was developed by Horst (1996). The criticism is briefly
recapitulated as follows. Suppose we represent Fodor's claim
schematically as:

(F) Mental state M means P because mental
representation MR means P.

Such a claim is most plausible under the assumption that the
expression “… means P” is univocal over the two
uses in (F)—i.e., that “… means P”
functions the same way when applied to mental states (such as beliefs,
desires, and occurrent judgments) and to mental representations (i.e.,
symbols in a language of thought). Under this assumption, the
“meaning” of mental representations is clearly a potential
explainer of the “meaning” of mental states, because it is
precisely the same property of “meaning” that is in
question in both instances.

However, this assumption is in tension with the assumption that the
kind of “meaning” attributed to mental representations is
the same as the kind of “meaning” that is attributed to
symbols such as utterances and inscriptions. There, the critics claim,
attributions of meaning, such as “This inscription meant
P” have a hidden complexity in their logical structure. The verb
‘means’ does not express simply a two-place relation
between inscription and its semantic value; rather, it must covertly
report either (a) speaker meaning, (b) hearer interpretation or (c)
interpretation licensed by a particular linguistic convention. As it
is specifically symbolic meaning—i.e.,
“meaning” in the sense of that word which is applied to
symbols and not some other use of the term—that
formalization and computation show us how to link to syntax, it is
important that it is this usage that is at work in CTM. But
if we assume that the phrase “MR
means P” in (F) must be cashed out in terms of speaker
meaning, hearer interpretation, or conventional interpretability, then
we are in none of these cases left with a potential explainer of the
kind of “meaning” that is ascribed to mental states. Each
of these notions is indeed conceptually dependent upon the notion of
meaningful mental states, and so one cannot explain mental meaning in
terms of symbolic meaning without being involved in an explanatory
circle.

There have been two sorts of explicit replies to this line of
criticism, and perhaps a third which is largely implicit. One line of
reply is to say that there is a univocal usage of the
semantic vocabulary: namely, what is supplied by
semantic theories such as Tarski's. On this view, “a
semantics” is simply a mapping from symbol-types onto their
extensions, or else an effective procedure for generating such a
mapping. However, such a view of “semantics” is arguably
too thin to be explanatory, as there are an indefinite number of
mapping relations, only a few of which are also semantic. (Cf.
Blackburn 1984, Field 1972, Horst 1996.) A second line of reply is to
look to an alternative “thicker” semantics, such as that
of C.S. Peirce. (Cf. von Eckardt 1993.) There has arguably not been
sufficient discussion of the relation between Peircean semantics and
the equivocity view which distinguishes “mental meaning”
(i.e., the sense of ‘meaning’ that can be applied to
mental states) from speaker meaning and conventional interpretability
of symbols. However, in one respect the Peircean strategy connects
with a third line of reply that computationalists have made, which
might also be a path to rapprochement between sides. Computationalists
have generally come to endorse causal theories of the semantics of
mental representations. Regardless of one's outlook on the general
prospects of causal theories of meaning, a sense of
“meaning” that is cashed out in terms of causal covariance
or causal etiology cannot be equivalent to either speaker meaning or
conventional interpretability. The good news for the computationalist
is that this may save the theory from explanatory circularity and
regress. However, it arguably carries the price of threatening CTM's
compatibility proof: the kind of “meaning” that is shown
to be capable of being tied to syntax in computing machines is the
conventional kind: e.g., that such-and-such a sequence of binary
digits is interpretable under certain conventions as representing a
particular integer. But if the kind of “meaning”
attributed to mental representations is not of this sort, then
computers have not shown that the relevant sorts of
“meaning” can be tied to syntax in the necessary
ways. Though this problem may not be insuperable, it should perhaps at
least be regarded as an open problem. Likewise, the viability of this
strategy is further dependent upon the prospects of a causal semantics
to explain the kind of “meaning” attributed to mental
states. (Cf. Horst, 1996)

Some early critics of CTM started from the observation that not all
processes are computable (that is, reducible to an algorithmic
solution), and concluded that computational explanations are only
possible for such mental processes as might turn out to be amenable to
algorithmic techniques. However, there is strong reason to believe that
there are problem domains that humans can think about and attain
knowledge in, but which are not formally computable.

The oldest line of argument here is due to J.R. Lucas (1961), who
has argued over a series of articles that the Gödel's
incompleteness theorem poses problems for the view that the mind is a
computer. More recently, Penrose (1989, 1990) has developed arguments
to the same conclusion. The basic line of these arguments is that human
mathematicians in fact understand and can prove more about arithmetic
than is computable. Therefore there must be more to (at least this kind
of) human cognition than mere computation. There has been extensive
debate on this topic over the past forty years (for example, see the
criticisms in Lewis [1969], [1979], [Bowie 1982] and [Feferman 1996]),
and the continued discussion suggests that the proper view to take of
this argument is still an open question.

A distinct line of argument was developed by Hubert Dreyfus (1972).
Dreyfus argued that most human knowledge and competence—particularly
expert knowledge—cannot in fact be reduced to an
algorithmic procedure, and hence is not computable in the relevant
technical sense. Drawing upon insights from Heidegger and existential
phenomenology, Dreyfus pointed to a principled difference between the
kind of cognition one might employ when learning a skill and the kind
employed by the expert. The novice chess player might follow rules like
“on the first move, advance the King's pawn two spaces”, “seek to
control the center”, and so on. But following such rules is
precisely the mark of the novice. The chess master simply
“sees” the “right move”. There at
least seems to be no rule-following involved, but merely a
skilled activity. (Since the original publication of What
Computers Can't Do in 1972, the play level of the best chess
computers has risen dramatically; however, it bears noting that the
brute force methods employed by champion chess computers seem to bear
little resemblance to either novice or expert play in humans.) Dreyfus
illustrates his claims with references to the problems faced by AI
researchers who attempted to codify expert knowledge into computer
programs. The success or failure here really has little to do with the
computing machinery, but with whether expert competence in
the domain in question can be captured in an algorithmic procedure. In
certain well-circumscribed domains this has succeeded; but more often
than not, argues Dreyfus, it is not possible to capture expert
knowledge in an algorithm, particularly where it draws upon general
background knowledge outside the problem domain.

There have been two main lines of response to Dreyfus's criticisms.
The first is to claim that Dreyfus is placing too much weight upon the
present state of work in AI, and drawing an inference about
all possible rule-based systems on the basis of the failures
of particular examples of a technology that is arguably still in its
infancy. Part of this criticism is surely correct: to the extent that
Dreyfus's argument is intended to be inductive, it is hasty
and, more importantly, vulnerable to refutation by future research.
Dreyfus might reply that the optimism of conventional AI researchers is
equally unsupported, but we must conclude that a purely inductive
generalization about what computers cannot do would provide only a weak
argument. However, Dreyfus's argument is not purely inductive; it also
contains a more principled claim about the nature of expert performance
and the unsuitability of rule-based techniques to duplicating
that performance. This argument is a complicated one, and has not
received decisive support or refutation among philosophers of mind.

The second line of reply to Dreyfus's arguments is to concede that
there may be a problem (perhaps even a principled problem) for a
certain type of system—e.g., a rule-based
system—but to claim that other types of systems avoid this
problem. Thus one might look to the “bottom-up” strategies
of connectionist networks or Rodney Brooks's attempts to build simple
insect-level intelligence as more promising approaches that side with
Dreyfus in criticizing the limitations of rule-based systems. Dreyfus
himself seems to have experimented with both sides of this
position. In an 1988 article with his brother Stuart, Dreyfus seemed
inclined to the view that neural networks stand in better stead in
this regard, and seem to handle some problem domains naturally wherein
rule-and-representation approaches have encountered problems. (He has
likewise endorsed aspects of Walter Freeman's attractor theory as
echoing some features of Merleau-Ponty's account of how a skilled
agent moves towards “maximum grip.”) However, in the 1992
version of What Computers Still Can't Do he opined that
“the neglected and then revived connectionist approach is merely
getting its deserved chance to fail” (xxxviii). More narrowly,
however, one might point out that this line of objection concedes
Dreyfus's real point, which was never that there could not be a piece
of intelligent hardware called a “computer”, but rather
that one could not build intelligence or cognition out of
“computation” in the sense of “rule-based symbol
manipulation.”

Perhaps the most influential criticism of CTM has been John Searle's
(1980) thought experiment known as the “Chinese Room”. In
this thought experiment, a human being is placed in the role of the
CPU in a computer. He is placed inside of a room with no way of
communicating with the outside except for symbolic communications that
come in through a slot in the room. These are written in Chinese, and
are meaningful inscriptions, albeit in a language he does not
understand. His task is to produce meaningful and appropriate
responses to the symbols he is handed in the form of Chinese
inscriptions of his own, which he passes out of the box. In this task
he is assisted by a rulebook, containing rules for what symbols to
write down in response to particular input conditions. This set-up is
designed to mimic in all respects the resources available to a digital
computer: it can receive symbolic input and produce symbolic output,
and it manipulates the symbols it receives on the basis of rules that
are such that they can be applied on non-semantic information like
syntax and symbol shape alone. The only difference in the thought
experiment is that the “processing unit” applying these
rules is a human being.

This experiment is directly a response to Alan Turing's suggestion
that we replace the question “Can machines think?” with
the question of whether they can succeed in an “imitation
game”, in which questioners are asked to determine, on the basis
of their answers alone, whether an unseen interlocutor is a person or
a machine. This test has come to be called the Turing
Test. Searle's response is, in essence, this: Let us assume for
purposes of conversation that it is possible to produce a
program—a set of rules—that would allow a machine that
followed these rules to pass the Turing test. Now, would the ability
to pass this test in and of itself be sufficient to establish
that the thing that passed the test was a thinking thing? Searle's
Chinese Room meets (by supposition) the criteria of a machine that can
pass the Turing Test in Chinese: it produces responses that are taken
to be meaningful and conversationally appropriate, and it does so by
wholly syntactic means. However, when we ask, “Does the Chinese
Room, or any portion thereof (say, the person inside)
understand the utterances?” the answer Searle quite naturally
urges upon us is “no”. Indeed, by supposition, the person
inside does not understand Chinese, and neither the rulebook nor the
human-rulebook-room system seems to be the right sort of thing to
attribute understanding to at all. The upshot is clear: even if it is
possible to simulate linguistic competence by purely computational
means, doing so does not provide sufficient conditions for
understanding.

The Chinese Room argument has enjoyed almost as much longevity as CTM
itself. It has spawned a small cottage industry of philosophical
articles, many of whose positions were presaged in the peer
commentaries printed with its seminal appearance in Behavioral and
Brain Sciences in 1980. Some critics are willing to bite the
bullet and claim that “understanding” can be
defined in wholly functional terms, and hence the Chinese
Room system really
does exhibit understanding. Others have conceded that the
apparatus Searle describes would not exhibit understanding, but have
argued that the addition of some further features—a robot
body, sensory apparatus, the ability to learn new rules and
information, or embedding it in a real environment with which to
interact—would thereby confer understanding. Searle and others
have adapted the thought experiment to extend the intuition that the
system lacks understanding in ways that are designed to incorporate
these variations. These adaptations, like the original experiment,
seem to elicit different intuitions in different readers; and so the
Chinese Room has remained one of the more troubling and
thought-provoking contributions to this literature.

Searle (1992) also argues that, on standard definitions of
computation, every object turns out to be a computer running a
program, because there is some possible interpretation of its states
that corresponds to the machine table for that program. (Searle
suggests that the wall behind him is, under the right interpretation,
running a particular word-processing program.) Searle's conclusion is
that an object is a computer, not in virtue of its intrinsic
properties, but only in relation to an interpretation: semantic and
even computational properties “are not intrinsic to the system at
all. They depend on an interpretation from the outside.” (Searle,
1992, 209) The mind may thus be a computer, in the sense of having
interpretations in terms of a machine table. However, since
intentionality is intrinsic to mental states, and does not depend upon
external interpretation, it cannot be accounted for in computational
terms. Putnam (1988) offers a similar argument in more formal terms
involving Finite State Automata. (Block 1978 puts forward related
arguments against functionalism in his “Chinese nation”
thought-experiment, wherein he argues that the nation of China can
implement a functional analysis for the mind of the sort embodied in a
machine table, yet is not thereby a thinking thing.)

Some aspects of Searle's characterization may be overstated. It is
controversial that every object can be described as running a
(non-trivial) program; and Searle's characterization of derived
intentionality sometimes make it sound as though this requires an
actual interpreter, as opposed to simply the availability of an
appropriate interpretation scheme. But the core of his objection (that
semantic and computational properties are extrinsic) does not really
require either of these claims. It is enough for his reductio argument
that there are clearly some adequately complex systems that would
count as computers on this definition, and that this violate our
intuitions about what should count as a computer. Likewise, his
argument can be made to work equally well without supposing an actual
interpreter, but only interpretability-in-principle. (Compare Horst
1996.)

Searle's and Putnam's articles have drawn direct responses, and
Searle's version of the argument is also based in assumptions about
extrinsic interpretation that are more generally controversial in
philosophy of mind. The principal line of response directed at these
articles has consisted in arguments that the Searle/Putnam conclusion
that every system of suitable complexity has an interpretation scheme
whereby it counts as a computer running a program can be reached only
by using an inappropriate notion of ‘computation’. The
“interpretation” under which the molecules in a wall count as a
computer running a word processing program is (a) completely ex
post facto (Copeland 1996), and, more importantly, (b) is
applicable only to the actual behavior of the molecules within a
certain (again, arbitrarily specified) timeframe, and does not capture
the counterfactual regularities of the causal behavior of a system
that performs computations by application of an algorithm. (Copeland
1996, Chalmers 1996, Scheutz 1999, Piccinini 2007) As several of these
writers allow, there are multiple definitions of
‘computation’ available in the literature, some of which
might be suitable to license the inferences made by Searle and Putnam.
What they claim in response, however, is (1) that there are stronger
definitions of ‘computation’ that include causal and/or
counterfactual properties, (2) that these additional properties are
implicit in standard characterizations of computers (for example, in
describing computation as being driven by application of algorithms),
and (3) that on these definitions, paradigm examples of computers
count as computers, but the kinds of examples Searle, Putnam and Block
cite in their reductio arguments do not.

Also potentially controversial is Searle's insistence that the
intentionality of mental states is “intrinsic”. Dennett
(1987), for example, has argued for an interpretivist semantics for
mental states as well, in which minds and other systems
“have” semantic properties and intentionality only as
viewed through the “intentional stance”. If Dennett is
right to reject intrinsic intentionality, a crucial premise of
Searle's argument is blocked. (See also the peer commentary on Searle
1990, and the entries on
consciousness and
consciousness and intentionality.)
There is no consensus
among philosophers as to whether an interpretivist semantics (whether
Dennett's or another, such as that of Donald Davidson (1984)) is
appropriate for mental states, and hence the viability of Searle's
argument is a matter on which the philosophical community is sharply
divided.

One cornerstone of Fodor's case for CTM was that some version of the
theory was implied by cognitivist theories of phenomena like learning
and language acquisition, and that these theories were the only
contenders we have in those domains. Critics of CTM have since argued
that there are now alternative accounts of most psychological
phenomena that do not require rule-governed reasoning in a language of
thought, and indeed seem at odds with it. Beginning in the late 1980s,
philosophers began to become aware of an alternative paradigm for
modeling psychological processes, sometimes called “neural
network” or “connectionist” approaches. Such
approaches had been pursued formally and empirically stemming from the
early work of Wiener and Rosenblatt, and carried on through the 1960s
until the present by researches such as Grossberg and Anderson. There
was some philosophical recognition of early cybernetic research (e.g.,
Sayre (1969, 1976)); however, neural network models entered the
philosophical mainstream only after the publication of Rumelhart and
McClelland's (1986) Parallel Distributed Processing.

Neural network models seek to model the dynamics of psychological
processes, not directly at the level of intentional states, but at the
level of the networks of neurons through which mental states are
(presumably) implemented. In some cases, psychological phenomena that
resisted algorithmic modeling at the cognitive level just seem to “fall
out” of the architecture of network models, or of network models of
particular design. Several types of learning in particular
seem to come naturally to network architectures, and more recently
researchers such as Smolensky have produced results suggesting that at
least some features of language acquisition can be simulated by his
models as well.

During the late 1980s and 1990s there was a great deal of
philosophical discussion of the relation between network and
computational models of the mind. Connectionist architectures were
contrasted with “classical” or “GOFAI”
(“good old-fashioned AI”) architectures employing rules
and symbolic representations. Advocates of connectionism, such as
Smolensky (1987), argued that connectionist models were importantly
distinct from classical computational models in that the processing
involved took place (and hence the relevant level of causal
explanation must be cast) at a sub-symbolic level, such as Smolensky's
tensor-product encoding. Unlike processing in a conventional computer,
the process is distributed rather than serial, there is no explicit
representation of the rules, and the representations are not
concatenative.

There is some general agreement that some of these differences do
not matter. Both sides are agreed, for example, that processes in the
brain are highly parallel and distributed. Likewise, even in
production-model computers, it is only in stored programs that rules
are themselves represented; the rules hard-wired into the CPU are not.
(The concatenative character is argued by some—e.g., van Gelder
[1991], Aydede [1997]—to be significant.)

The most important “classicist” response is that of Fodor
and Pylyshyn [1988]. (See also Fodor and McLaughlin [1990].) They
argue that any connectionist system that could guarantee systematicity
and productivity would simply be an implementation of a classical
(LOT) architecture. Much turns, however, on exactly what features are
constitutive of a classical architecture. Van Gelder (1991), for
example, claims that classicists are committed to specifically
“concatenative compositionality” [Van Gelder
1991, p. 365)—i.e., compositionality in a linear sequence like
a sentence rather than in a multi-dimensional space like Smolensky's
tensor-product model—and that this means that they explain
cognitive features without being merely “implementational”
and hence provide a significantly different alternative to
classicism. In response, Aydede [1997], while recognizing the tendency
of classicists to make assumptions that the LOT is concatenative,
argues that the LOT need not be held to this stronger
criterion. (Compare Loewer and Rey, 1991.) However, if one allows
non-concatenative systems like Smolensky's tensor space or Pollack's
Recursive Auto-Associative Memory to count as examples or
implementations of LOT, more attention is needed to how the notion of
a “language” of thought places constraints upon what types
of “representations” are included in and excluded from the
family of LOT models. There has been no generally-agreed-to resolution
of this particular dispute, and while it has ceased to generate a
steady stream of articles, it should be classified as an “open
question”.

In terms of the case for CTM, recognition of alternative network
models (and other alternative models, such as the dynamics systems
approach of van Gelder) has at least undercut the “only game in
town” argument. In the present dialectical situation, advocates
of CTM must additionally clarify the relationship of their models to
network models, and argue that their models are better as accounts of
how things are done in the human mind and brain in particular problem
domains. Some of the particulars of this project of clarifying the
relations between classical and connectionist computational
architectures are also discussed in the entry on
connectionism.

The criticisms that have been canvassed here arguably do not threaten
the CTM's claim to presenting a compatibility proof for intentional
realism with physicalism, at least in the cases of kinds of
understanding that can be formalized. This goal—of
“vindicating” psychology by demonstrating its
compatibility with the generality of physics—was itself a
prominent part of the computationalist movement in philosophy, and
explains why representational/computational theories were often seen
as the main alternatives to eliminative materialism in the 1980s and
1990s.

However, this goal itself, and the corresponding commitment to a
particular kind of naturalization of the mind, is insufficiently
subjected to scrutiny on the current scene. We might pose the question
like this: if push were to come to shove between one's commitment to
the results of some empirical science like psychology and one's
commitment to a metaphysical position (like materialism) or metatheory
about science (like some form of the Unity of Science hypothesis),
which ought to give way to the other? It is, in a way, curious that
Fodor, an important defender of the autonomy of the special sciences,
especially psychology (cf. Fodor 1974), seems in this instance to
defer to metaphysical or metatheoretical commitments here, and hence
views psychology as standing in need of vindication. By contrast,
philosophers of science have, since the 1970s, increasingly been
inclined to reject any metatheoretical and metaphysical standards
imposed upon science from without, and more specifically have tended
to favor the autonomy of local “special” sciences over
assumptions that the sciences must fit together in some particular
fashion. (In such a spirit, CTM's original proponent, Hilary Putnam,
has more recently embraced a pragmatist pluralism.) It is possible
that in another decade the recent preoccupation with vindicating
psychology will be regarded as one of the last vestiges of the
Positivist Unity of Science movement.

In the 1980s, CTM faced a major crisis with the growing popularity of
externalist theories of semantics. (See entry
externalism about mental content.)
Externalists hold that mental content
is not, or at least is not entirely, within the head or within the
mind. For example, the fact that water is H2O is (allegedly) part of
the meaning of ‘water’, even if a speaker does not know
that water is H2O, and ‘elm’ refers to a certain type of
tree even if a speaker does not know how to identify elms and cannot
even distinguish elms from beeches. Such components of meaning as are
external to the mind cannot be narrowly located in representations
existing completely within the mind. And to the extent that CTM is
committed to confining computation to symbols existing entirely within
the mind, there seems to be a tension between CTM and externalist
theories of content.

Fodor [1993] and others responded to this concern by combining CTM
with a causal account of mental content, on which a mental
representation R means X just in case R-tokenings
are reliably caused by Xs: for example the concept COW is
reliably caused by perceptual access to cattle and the concept WATER
by contact with H2O. This does not require that the representational
and computational resources of the system encode the fact that the
concept WATER refers to a particular molecular kind, nor all of the
properties that H2O actually possesses. Nor does it require that all
of the inferences licensed by its rules are true. (For example, it
might have rules that generate tokenings of “water is a simple
Aristotelian element”.) What is required is only that the
semantic and propositional understanding (or misunderstanding) that
the system possesses be accounted for in terms of syntactic features
of the representations and syntactically-based inference rules. This,
moreover, seems consonant with the fact that real human beings often
misrepresent or are ignorant of the properties of the things they
think and talk about. It does, however, require that an adequate
account of content include a “narrow” component (encoded
in syntax and located in the mind or brain) as well as a
“broad” component (determining reference), and that
psychological explanation of reasoning the “narrow”
properties.

An externalist version of CTM essentially holds that the mind is a
computer, but that some aspects of content are “farmed
out” to an environment with which the mind causally interacts.
On this view, there are clear mind/world divisions, and even divisions
between such parts of the body as are involved in computation (e.g.,
the brain, or subareas of the brain) and the rest of the biological
organism. Indeed, computationalists generally treat it as
fundamentally important that cognitively and computationally
equivalent systems could be realized in different media: for example,
a human being, a human brain coupled with a robotic body, a computing
machine in a robotic body, or a computing machine interacting with a
virtual environment.

A more radical externalist thesis is that cognition is essentially
embodied and embedded. Perception, action, and even imagination and
reasoning are “embodied”, not only in the sense of being
realized through some physical system, but in the stronger sense that
they involve bodily processes that extend beyond the brain into the
nervous system and even into other tissue and to biochemical processes
in the body. At the same time, even the brain processes involved in
cognition involve non-representational, non-computational skills of
bodily know-how. The mind is also “embedded” in its
environment, not only in the sense of interacting with it causally
through perceptual “inputs” and behavioral
“outputs”, but in the more radical sense that things
outside the physical organism—from tools to prostheses to
books and websites—are integrally part of cognition itself.
We are, as Andy Clark puts it, already “natural-born
cyborgs.” [Clark 1997, 2000, 2001, 2005; Clark and Chalmers
1998]

The relationship between CTM and embodied and embedded cognition can
be viewed in two ways. On the one hand, these views might be seen as
claiming, in effect, that there are aspects of cognition that are
non-representational and non-computational. This, however, is fully
compatible with a modest version of CTM that claims only that some
aspects of cognition are representational and computational.
Moreover, the computationalist can attempt to construe bodily skill as
itself computational (though again see [Dreyfus 1979] for seminal
discussion of problems), and extend computation to include external
symbols. [Wilson 1994] On the other hand, embodied and embedded
cognition might be seen as an alternative general framework for
understanding cognition: not just a supplementary account of
“parts” of cognition that CTM leaves out, but a
fundamentally different theoretical framework.

Putnam, Hilary, 1960. “Minds and Machines,” In Dimensions of
Mind, edited by S. Hook. New York: New York University Press.

Putnam, Hilary, 1961. “Brains and Behavior”, originally read as
part of the program of the American Association for the Advancement of
Science, Section L (History and Philosophy of Science), December 27,
1961. Reprinted in Block (1980).

Von Neumann, John, “First Draft of a Report on the
EDVAC,” Contract No. W-670-ORD-4926, Between
the United States Army Ordnance Department and the University
of Pennsylvania Moore School of Electrical Engineering,
University of Pennsylvania. June 30, 1945.